Practice Real-time Data Pipelines (ETL) - 3.2.1 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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3.2.1 - Real-time Data Pipelines (ETL)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ETL stand for in data processing?

πŸ’‘ Hint: Think about the process of moving data.

Question 2

Easy

Name a key feature of Apache Kafka.

πŸ’‘ Hint: Consider its performance in handling messages.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary purpose of MapReduce?

  • Real-time data processing
  • Batch processing of large datasets
  • Stream analytics

πŸ’‘ Hint: Focus on its main application area.

Question 2

True or False: Apache Kafka ensures message ordering across all partitions.

  • True
  • False

πŸ’‘ Hint: Think about how Kafka organizes messages.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design an ETL pipeline using Apache Kafka as the core. Explain how you would handle fault tolerance and data durability.

πŸ’‘ Hint: Consider how Kafka’s architecture supports multiple use cases.

Question 2

Compare the performance implications of using MapReduce versus Spark for a real-time analytics task. What factors should be taken into account?

πŸ’‘ Hint: Think about how speed and data retrieval methods affect performance.

Challenge and get performance evaluation